In this paper, we propose a simple yet effective training strategy dubbed Convex Combination Consistency Between Neighbors (C\(^3\) BN), which aims to achieve robust snippet predictions.
The core idea of C\(^3\) BN is to leverage the inherent smoothness within the videos to perform effective model regularization. By 'smoothness', we mean that the scenes in real-world video usually change smoothly.
Based on this, we propose a microBy ‘micro’, we mean the proposed data augmentation strategy is for snippets rather than videos. data augmentation strategy, where we mix the pairs of adjacent snippets (dubbed parent snippets) by convex combination and generate a set of new snippets (dubbed child snippets). The operation is borrowed from MixUp [1]}, but there are some task-specific insights. Specifically, due to the natural smoothness within videos, the child snippets have two important properties. 1) They are similar to realistic snippets, since the adjacent snippets are usually visually similar. 2) Their temporal locations lie in-between the parent snippets. More importantly, compared with the parent snippets, the child snippets have more diverse temporal locations. These two properties give the potential for exploring more patterns in-between adjacent snippets in the latent representations, thereby boosting the generalization power of the model to data points in-between adjacent snippets.
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